{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"vscode":{"interpreter":{"hash":"6ad33e096e05aa34a53a020c7212c2ebeaad0c283eb3c6a43a9b0f5ae37d0a4a"}}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-05-21T01:26:56.627006Z","iopub.status.busy":"2023-05-21T01:26:56.626382Z","iopub.status.idle":"2023-05-21T01:27:00.537833Z","shell.execute_reply":"2023-05-21T01:27:00.535936Z","shell.execute_reply.started":"2023-05-21T01:26:56.626949Z"},"trusted":true},"execution_count":18,"outputs":[{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[18], line 12\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Input data files are available in the read-only \"../input/\" directory\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dirname, _, filenames \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39mwalk(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/input\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m filenames:\n\u001b[1;32m     14\u001b[0m         \u001b[38;5;28mprint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dirname, filename))\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:377\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 377\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[43mentry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m    379\u001b[0m     \u001b[38;5;66;03m# If is_dir() raises an OSError, consider that the entry is not\u001b[39;00m\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;66;03m# a directory, same behaviour than os.path.isdir().\u001b[39;00m\n\u001b[1;32m    381\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\nbatch_size = 32\ntrain_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\ntest_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)","metadata":{"execution":{"iopub.status.busy":"2023-06-25T06:47:25.879491Z","iopub.execute_input":"2023-06-25T06:47:25.880166Z","iopub.status.idle":"2023-06-25T06:47:35.978611Z","shell.execute_reply.started":"2023-06-25T06:47:25.880116Z","shell.execute_reply":"2023-06-25T06:47:35.977593Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\n\n# functions to show an image\n\n\ndef imshow(img):\n    img = img / 2 + 0.5     # unnormalize\n    npimg = img.numpy()\n    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n    plt.show()\n\n\n# get some random training images\ndataiter = iter(train_loader)\n# images, labels = dataiter.next()\nimages, labels = next(dataiter)\n\n# show images\nimshow(torchvision.utils.make_grid(images))\n# print labels\nprint(labels.shape)","metadata":{"execution":{"iopub.status.busy":"2023-06-25T06:49:31.234567Z","iopub.execute_input":"2023-06-25T06:49:31.235862Z","iopub.status.idle":"2023-06-25T06:49:31.861254Z","shell.execute_reply.started":"2023-06-25T06:49:31.235819Z","shell.execute_reply":"2023-06-25T06:49:31.860111Z"},"trusted":true},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"torch.Size([32])\n","output_type":"stream"}]},{"cell_type":"code","source":"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"3x3 convolution with padding\"\n    return nn.Conv2d(\n        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n    )\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n      \nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass Res2NetBottleneck(nn.Module):\n\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4\n    ):\n        super(Res2NetBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        \n#         print(out.shape)\n#         print(\"Begin Forward\")\n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out    \n\nclass SELayer(nn.Module):\n    def __init__(self, channel, reduction=16):\n        super(SELayer, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)        #全局平均池化，输入BCHW -> 输出 B*C*1*1\n        self.fc = nn.Sequential(\n            nn.Linear(channel, channel // reduction, bias=False), #可以看到channel得被reduction整除，否则可能出问题\n            nn.ReLU(inplace=True),\n            nn.Linear(channel // reduction, channel, bias=False),\n            nn.Sigmoid()\n        )\n \n    def forward(self, x):\n        b, c, _, _ = x.size()\n        y = self.avg_pool(x).view(b, c)   #得到B*C*1*1,然后转成B*C，才能送入到FC层中。\n        y = self.fc(y).view(b, c, 1, 1)   #得到B*C的向量，C个值就表示C个通道的权重。把B*C变为B*C*1*1是为了与四维的x运算。\n        return x * y.expand_as(x)         #先把B*C*1*1变成B*C*H*W大小，其中每个通道上的H*W个值都相等。*表示对应位置相乘。\n    \nclass SEBottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, reduction=4\n    ):\n        super(SEBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.se = SELayer(planes * 4, reduction)\n        self.downsample = downsample\n        self.stride = stride\n        self.reduction = reduction\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n            \n        out = self.se(out)\n        \n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass ColaBasicBlock(nn.Module):\n\n    expansion = 4\n    \n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4, reduction=4\n    ):\n        super(ColaBasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.reduction = reduction\n        self.mix = nn.ModuleList([nn.Conv2d(planes, planes//self.reduction, kernel_size=1, stride=1, bias=False), \n                                  nn.BatchNorm2d(planes//self.reduction),\n                                  nn.Conv2d(planes//self.reduction, planes, kernel_size=1, stride=1, bias=False),\n                                  nn.BatchNorm2d(planes),\n                                  nn.ReLU(inplace=True)])\n        self.conv2 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        for block in self.mix:\n            out = block(out)\n#         print(out.shape)\n#         print(\"Begin Forward\")\n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        \n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out    \n    \nclass ColaSELayer(nn.Module):\n    def __init__(self, channel, reduction=16, scales=4):\n        super(ColaSELayer, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)        #全局平均池化，输入BCHW -> 输出 B*C*1*1\n        self.conv = nn.ModuleList([nn.Conv2d(channel//scales, channel//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn = nn.ModuleList([nn.BatchNorm2d(channel//scales) for _ in range(scales-1)])\n        self.relu = nn.ReLU(inplace=True)\n        self.fc = nn.ModuleList([nn.Sequential(\n            nn.Linear(channel // scales, channel // reduction, bias=False), #可以看到channel得被reduction整除，否则可能出问题\n            nn.ReLU(inplace=True),\n            nn.Linear(channel // reduction, channel//scales, bias=False),\n            nn.Sigmoid()\n        ) for _ in range(scales)] )\n        self.scales = scales\n \n    def forward(self, x):\n        xs = torch.chunk(x, self.scales, 1)\n        ys = []\n        for s in range(self.scales):\n            b, c, _, _ = xs[s].size()\n            if s==0:\n                ys.append(self.fc[0](self.avg_pool(xs[0]).view(b,c)).view(b,c,1,1))\n            elif s==1:\n                ys.append(self.fc[1](self.avg_pool(self.relu(self.bn[0](self.conv[0](xs[1])))).view(b,c)).view(b,c,1,1))\n            else:\n                ys.append(self.fc[s](self.avg_pool(self.relu(self.bn[s-1](self.conv[s-1](xs[s]+ys[-1])))).view(b,c)).view(b,c,1,1))\n        \n        y = torch.cat(ys, 1)\n        return x * y.expand_as(x)         #先把B*C*1*1变成B*C*H*W大小，其中每个通道上的H*W个值都相等。*表示对应位置相乘。\n    \nclass ColaSEBottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, reduction=4\n    ):\n        super(ColaSEBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.se = ColaSELayer(planes * 4, reduction)\n        self.downsample = downsample\n        self.stride = stride\n        self.reduction = reduction\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n            \n        out = self.se(out)\n        \n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass h_swish(nn.Module):\n    def __init__(self, inplace=True):\n        super(h_swish, self).__init__()\n        self.sigmoid = h_sigmoid(inplace=inplace)\n\n    def forward(self, x):\n        return x * self.sigmoid(x)\n    \nclass ColaBranchBottleneck(nn.Module):\n    \n    expansion = 4\n    \n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4, reduction=32\n    ):\n        super(ColaBranchBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.pool_w = nn.AdaptiveAvgPool2d((1, None))\n        self.pool_h = nn.AdaptiveAvgPool2d((None,1))\n        \n        self.conv2_1 = nn.Conv2d(planes, planes//reduction, kernel_size=1, stride=1, bias=False)\n        self.bn2_1 = nn.BatchNorm2d(planes//reduction)\n        self.conv2_2 = nn.Conv2d(planes//reduction, planes, kernel_size=1, stride=1, bias=False)\n        self.bn2_2 = nn.BatchNorm2d(planes)\n        self.pool_wh = nn.AdaptiveAvgPool2d((1, None))\n        \n        self.conv3 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn3 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv4 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn4 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n        self.reduction = reduction\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        \n#         print(out.shape)\n#         print(\"Begin Forward\")\n        out_h = self.pool_h(out)\n        out_w = self.pool_w(out).permute(0, 1, 3, 2)\n        out_cat = torch.cat([out_h, out_w], dim=2)\n        out2_1 = self.bn2_1(self.conv2_1(out_cat))\n        out2_2 = self.bn2_2(self.conv2_2(out2_1))\n        out2 = torch.sigmoid(self.pool_wh(out2_2))\n        out = out*out2\n        \n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn3[s-1](self.conv3[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn3[s-1](self.conv3[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv4(out)\n        out = self.bn4(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out \n    \nclass ResNet(nn.Module):\n    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n        self.inplanes = 64\n        super(ResNet, self).__init__()\n        self.network_type = network_type\n        # different model config between ImageNet and CIFAR\n        if network_type == \"ImageNet\":\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n            )\n            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n            self.avgpool = nn.AvgPool2d(7)\n        else:\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n            )\n\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n        self.layer2 = self._make_layer(\n            block, 128, layers[1], stride=2, att_type=att_type\n        )\n        self.layer3 = self._make_layer(\n            block, 256, layers[2], stride=2, att_type=att_type\n        )\n        self.layer4 = self._make_layer(\n            block, 512, layers[3], stride=2, att_type=att_type\n        )\n\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        init.kaiming_normal_(self.fc.weight)\n        for key in self.state_dict():\n            if key.split(\".\")[-1] == \"weight\":\n                if \"conv\" in key:\n                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n                if \"bn\" in key:\n                    if \"SpatialGate\" in key:\n                        self.state_dict()[key][...] = 0\n                    else:\n                        self.state_dict()[key][...] = 1\n            elif key.split(\".\")[-1] == \"bias\":\n                self.state_dict()[key][...] = 0\n\n    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(\n                    self.inplanes,\n                    planes * block.expansion,\n                    kernel_size=1,\n                    stride=stride,\n                    bias=False,\n                ),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(\n            block(\n                self.inplanes,\n                planes,\n                stride,\n                downsample,\n                use_triplet_attention=att_type == \"TripletAttention\",\n            )\n        )\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(\n                block(\n                    self.inplanes,\n                    planes,\n                    use_triplet_attention=att_type == \"TripletAttention\",\n                )\n            )\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        if self.network_type == \"ImageNet\":\n            x = self.maxpool(x)\n\n        x = self.layer1(x)\n#         print(\"Begin layer2\")\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        if self.network_type == \"ImageNet\":\n            x = self.avgpool(x)\n        else:\n            x = F.avg_pool2d(x, 4)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n        return x\n\n\ndef ResidualNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(SEBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n#         model = ResNet(BasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n#         model = ResNet(Bottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(BasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n        # model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Bottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n  \ndef Res2Net(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(Res2NetBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Res2NetBottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\ndef ColaNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n#         model = ResNet(ColaBasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n#         model = ResNet(ColaSEBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n         model = ResNet(ColaBranchBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n    elif depth == 34:\n        model = ResNet(ColaBasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(ColaBasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(ColaBasicBlock, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n\ndevice=\"cuda\"\nres2net=Res2Net(\"CIFAR100\",18,100,None).to(device)\nprint(res2net)","metadata":{"execution":{"iopub.status.busy":"2023-06-25T08:03:46.081678Z","iopub.execute_input":"2023-06-25T08:03:46.082180Z","iopub.status.idle":"2023-06-25T08:03:46.986336Z","shell.execute_reply.started":"2023-06-25T08:03:46.082136Z","shell.execute_reply":"2023-06-25T08:03:46.980781Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_bottleneck = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_bottleneck)","metadata":{"execution":{"iopub.status.busy":"2023-05-24T00:30:38.680025Z","iopub.status.idle":"2023-05-24T00:30:38.681158Z","shell.execute_reply":"2023-05-24T00:30:38.680930Z","shell.execute_reply.started":"2023-05-24T00:30:38.680905Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"resnet_basicblock = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_basicblock)","metadata":{"execution":{"iopub.execute_input":"2023-05-21T02:43:13.742599Z","iopub.status.busy":"2023-05-21T02:43:13.742246Z","iopub.status.idle":"2023-05-21T02:43:13.974772Z","shell.execute_reply":"2023-05-21T02:43:13.973855Z","shell.execute_reply.started":"2023-05-21T02:43:13.742572Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","output_type":"stream","text":"ResNet(\n\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n  (relu): ReLU(inplace=True)\n\n  (layer1): Sequential(\n\n    (0): BasicBlock(\n\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n    )\n\n    (1): BasicBlock(\n\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n    )\n\n  )\n\n  (layer2): Sequential(\n\n    (0): BasicBlock(\n\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (downsample): Sequential(\n\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      )\n\n    )\n\n    (1): BasicBlock(\n\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n    )\n\n  )\n\n  (layer3): Sequential(\n\n    (0): BasicBlock(\n\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (downsample): Sequential(\n\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      )\n\n    )\n\n    (1): BasicBlock(\n\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n    )\n\n  )\n\n  (layer4): Sequential(\n\n    (0): BasicBlock(\n\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (downsample): Sequential(\n\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      )\n\n    )\n\n    (1): BasicBlock(\n\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n    )\n\n  )\n\n  (fc): Linear(in_features=512, out_features=100, bias=True)\n\n)\n"}]},{"cell_type":"code","source":"resnet_se = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_se)","metadata":{"execution":{"iopub.execute_input":"2023-05-21T08:00:34.604096Z","iopub.status.busy":"2023-05-21T08:00:34.603506Z","iopub.status.idle":"2023-05-21T08:00:34.979456Z","shell.execute_reply":"2023-05-21T08:00:34.978439Z","shell.execute_reply.started":"2023-05-21T08:00:34.604065Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stdout","output_type":"stream","text":"ResNet(\n\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n  (relu): ReLU(inplace=True)\n\n  (layer1): Sequential(\n\n    (0): SEBottleneck(\n\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (se): SELayer(\n\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n\n        (fc): Sequential(\n\n          (0): Linear(in_features=256, out_features=64, bias=False)\n\n          (1): ReLU(inplace=True)\n\n          (2): Linear(in_features=64, out_features=256, bias=False)\n\n          (3): Sigmoid()\n\n        )\n\n      )\n\n      (downsample): Sequential(\n\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      )\n\n    )\n\n    (1): SEBottleneck(\n\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (se): SELayer(\n\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n\n        (fc): Sequential(\n\n          (0): Linear(in_features=256, out_features=64, bias=False)\n\n          (1): ReLU(inplace=True)\n\n          (2): Linear(in_features=64, out_features=256, bias=False)\n\n          (3): Sigmoid()\n\n        )\n\n      )\n\n    )\n\n  )\n\n  (layer2): Sequential(\n\n    (0): SEBottleneck(\n\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (se): SELayer(\n\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n\n        (fc): Sequential(\n\n          (0): Linear(in_features=512, out_features=128, bias=False)\n\n          (1): ReLU(inplace=True)\n\n          (2): Linear(in_features=128, out_features=512, bias=False)\n\n          (3): Sigmoid()\n\n        )\n\n      )\n\n      (downsample): Sequential(\n\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      )\n\n    )\n\n    (1): SEBottleneck(\n\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (se): SELayer(\n\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n\n        (fc): Sequential(\n\n          (0): Linear(in_features=512, out_features=128, bias=False)\n\n          (1): ReLU(inplace=True)\n\n          (2): Linear(in_features=128, out_features=512, bias=False)\n\n          (3): Sigmoid()\n\n        )\n\n      )\n\n    )\n\n  )\n\n  (layer3): Sequential(\n\n    (0): SEBottleneck(\n\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (se): SELayer(\n\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n\n        (fc): Sequential(\n\n          (0): Linear(in_features=1024, out_features=256, bias=False)\n\n          (1): ReLU(inplace=True)\n\n          (2): Linear(in_features=256, out_features=1024, bias=False)\n\n          (3): Sigmoid()\n\n        )\n\n      )\n\n      (downsample): Sequential(\n\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      )\n\n    )\n\n    (1): SEBottleneck(\n\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (se): SELayer(\n\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n\n        (fc): Sequential(\n\n          (0): Linear(in_features=1024, out_features=256, bias=False)\n\n          (1): ReLU(inplace=True)\n\n          (2): Linear(in_features=256, out_features=1024, bias=False)\n\n          (3): Sigmoid()\n\n        )\n\n      )\n\n    )\n\n  )\n\n  (layer4): Sequential(\n\n    (0): SEBottleneck(\n\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (se): SELayer(\n\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n\n        (fc): Sequential(\n\n          (0): Linear(in_features=2048, out_features=512, bias=False)\n\n          (1): ReLU(inplace=True)\n\n          (2): Linear(in_features=512, out_features=2048, bias=False)\n\n          (3): Sigmoid()\n\n        )\n\n      )\n\n      (downsample): Sequential(\n\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      )\n\n    )\n\n    (1): SEBottleneck(\n\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n      (relu): ReLU(inplace=True)\n\n      (se): SELayer(\n\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n\n        (fc): Sequential(\n\n          (0): Linear(in_features=2048, out_features=512, bias=False)\n\n          (1): ReLU(inplace=True)\n\n          (2): Linear(in_features=512, out_features=2048, bias=False)\n\n          (3): Sigmoid()\n\n        )\n\n      )\n\n    )\n\n  )\n\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n\n)\n"}]},{"cell_type":"code","source":"cola_net = ColaNet(\"CIFAR100\",18,100,None).to(device)\nprint(cola_net)","metadata":{"execution":{"iopub.status.busy":"2023-06-25T08:03:56.245268Z","iopub.execute_input":"2023-06-25T08:03:56.245632Z","iopub.status.idle":"2023-06-25T08:03:56.731311Z","shell.execute_reply.started":"2023-06-25T08:03:56.245602Z","shell.execute_reply":"2023-06-25T08:03:56.730373Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): ColaBranchBottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_w): AdaptiveAvgPool2d(output_size=(1, None))\n      (pool_h): AdaptiveAvgPool2d(output_size=(None, 1))\n      (conv2_1): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_1): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2_2): Conv2d(2, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_wh): AdaptiveAvgPool2d(output_size=(1, None))\n      (conv3): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn3): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBranchBottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_w): AdaptiveAvgPool2d(output_size=(1, None))\n      (pool_h): AdaptiveAvgPool2d(output_size=(None, 1))\n      (conv2_1): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_1): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2_2): Conv2d(2, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_wh): AdaptiveAvgPool2d(output_size=(1, None))\n      (conv3): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn3): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): ColaBranchBottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_w): AdaptiveAvgPool2d(output_size=(1, None))\n      (pool_h): AdaptiveAvgPool2d(output_size=(None, 1))\n      (conv2_1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2_2): Conv2d(4, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_wh): AdaptiveAvgPool2d(output_size=(1, None))\n      (conv3): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn3): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv4): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBranchBottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_w): AdaptiveAvgPool2d(output_size=(1, None))\n      (pool_h): AdaptiveAvgPool2d(output_size=(None, 1))\n      (conv2_1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2_2): Conv2d(4, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_wh): AdaptiveAvgPool2d(output_size=(1, None))\n      (conv3): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn3): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv4): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): ColaBranchBottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_w): AdaptiveAvgPool2d(output_size=(1, None))\n      (pool_h): AdaptiveAvgPool2d(output_size=(None, 1))\n      (conv2_1): Conv2d(256, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2_2): Conv2d(8, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_wh): AdaptiveAvgPool2d(output_size=(1, None))\n      (conv3): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn3): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv4): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn4): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBranchBottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_w): AdaptiveAvgPool2d(output_size=(1, None))\n      (pool_h): AdaptiveAvgPool2d(output_size=(None, 1))\n      (conv2_1): Conv2d(256, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2_2): Conv2d(8, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_wh): AdaptiveAvgPool2d(output_size=(1, None))\n      (conv3): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn3): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv4): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn4): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): ColaBranchBottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_w): AdaptiveAvgPool2d(output_size=(1, None))\n      (pool_h): AdaptiveAvgPool2d(output_size=(None, 1))\n      (conv2_1): Conv2d(512, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2_2): Conv2d(16, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_wh): AdaptiveAvgPool2d(output_size=(1, None))\n      (conv3): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn3): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv4): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn4): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBranchBottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_w): AdaptiveAvgPool2d(output_size=(1, None))\n      (pool_h): AdaptiveAvgPool2d(output_size=(None, 1))\n      (conv2_1): Conv2d(512, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2_2): Conv2d(16, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn2_2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (pool_wh): AdaptiveAvgPool2d(output_size=(1, None))\n      (conv3): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn3): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv4): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn4): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"import torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\n# 这里需要修改优化器\noptimizer = torch.optim.Adam(cola_net.parameters(), lr=0.005)\nprint(len(train_loader))","metadata":{"execution":{"iopub.status.busy":"2023-06-25T08:03:59.330632Z","iopub.execute_input":"2023-06-25T08:03:59.331336Z","iopub.status.idle":"2023-06-25T08:03:59.344753Z","shell.execute_reply.started":"2023-06-25T08:03:59.331303Z","shell.execute_reply":"2023-06-25T08:03:59.343549Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"1250\n","output_type":"stream"}]},{"cell_type":"code","source":"from tqdm import tqdm\ndef train(net, epoch):\n    net.train()\n    # Loop over each batch from the training set\n    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n    for batch_idx, (data, target) in enumerate(train_tqdm):\n        # Copy data to GPU if needed\n        data = data.to(device)\n        target = target.to(device)\n        # Zero gradient buffers\n        optimizer.zero_grad()\n        # Pass data through the network\n        output = net(data)\n        # Calculate loss\n        loss = criterion(output, target)\n        # Backpropagate\n        loss.backward()\n        # Update weights\n        optimizer.step()  # w - alpha * dL / dw\n        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n    return net\n\ndef validate(net, lossv,top1AccuracyList,top5AccuracyList):\n    net.eval()\n    val_loss = 0\n    top1Correct = 0\n    top5Correct = 0\n    for index,(data, target) in enumerate(test_loader):\n        data = data.to(device)\n        labels = target.to(device)\n        outputs = net(data)\n        val_loss += criterion(outputs, labels).data.item()\n        _, top1Predicted = torch.max(outputs.data, 1)\n        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n        top1Correct += (top1Predicted == labels).cpu().sum().item()\n        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n\n    val_loss /= len(test_loader)\n    lossv.append(val_loss)\n\n    top1Acc=100*top1Correct / len(test_loader.dataset)\n    top5Acc=100*top5Correct / len(test_loader.dataset)\n    top1AccuracyList.append(top1Acc)\n    top5AccuracyList.append(top5Acc)\n    \n    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n#     accv.append(accuracy)","metadata":{"execution":{"iopub.status.busy":"2023-06-25T08:04:00.631190Z","iopub.execute_input":"2023-06-25T08:04:00.631542Z","iopub.status.idle":"2023-06-25T08:04:00.644909Z","shell.execute_reply.started":"2023-06-25T08:04:00.631513Z","shell.execute_reply":"2023-06-25T08:04:00.643780Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"import time\n\nres2netLossv = []\nres2netTop1AccuracyList = []   # top1准确率列表\nres2netTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    res2net = train(res2net, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(res2net, res2netLossv,res2netTop1AccuracyList,res2netTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))\n","metadata":{"execution":{"iopub.status.busy":"2023-06-25T06:54:24.049228Z","iopub.execute_input":"2023-06-25T06:54:24.049593Z","iopub.status.idle":"2023-06-25T07:00:37.664634Z","shell.execute_reply.started":"2023-06-25T06:54:24.049554Z","shell.execute_reply":"2023-06-25T07:00:37.663437Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [01:14<00:00, 16.77it/s, loss=5.93]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 5.4659, Top1Accuracy: 109/10000 (1%) Top5Accuracy: 505.0/10000 (5%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [01:03<00:00, 19.83it/s, loss=5.12]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 5.4948, Top1Accuracy: 103/10000 (1%) Top5Accuracy: 484.0/10000 (5%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [01:02<00:00, 19.90it/s, loss=5.23]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 5.4143, Top1Accuracy: 102/10000 (1%) Top5Accuracy: 477.0/10000 (5%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [01:03<00:00, 19.73it/s, loss=5.57]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 5.3969, Top1Accuracy: 108/10000 (1%) Top5Accuracy: 493.0/10000 (5%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [01:03<00:00, 19.73it/s, loss=5.28]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 5.4348, Top1Accuracy: 110/10000 (1%) Top5Accuracy: 484.0/10000 (5%)\n\nFinished Training\nTraining complete in 5m 27s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_bottleneckLossv = []\nresnet_bottleneckTop1AccuracyList = []   # top1准确率列表\nresnet_bottleneckTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_bottleneck = train(resnet_bottleneck, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_bottleneck, resnet_bottleneckLossv,resnet_bottleneckTop1AccuracyList,resnet_bottleneckTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.execute_input":"2023-05-21T02:28:46.023917Z","iopub.status.busy":"2023-05-21T02:28:46.022872Z","iopub.status.idle":"2023-05-21T02:35:44.185983Z","shell.execute_reply":"2023-05-21T02:35:44.184783Z","shell.execute_reply.started":"2023-05-21T02:28:46.023866Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stderr","output_type":"stream","text":"Epoch 0: 100%|██████████| 1250/1250 [01:16<00:00, 16.41it/s, loss=3.08]\n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 4.4866, Top1Accuracy: 3599/10000 (36%) Top5Accuracy: 6654.0/10000 (67%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 1: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.47]\n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.7050, Top1Accuracy: 3805/10000 (38%) Top5Accuracy: 6840.0/10000 (68%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 2: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.54]\n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.3556, Top1Accuracy: 4170/10000 (42%) Top5Accuracy: 7197.0/10000 (72%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 3: 100%|██████████| 1250/1250 [01:16<00:00, 16.37it/s, loss=2.09] \n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.2923, Top1Accuracy: 4282/10000 (43%) Top5Accuracy: 7332.0/10000 (73%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 4: 100%|██████████| 1250/1250 [01:15<00:00, 16.56it/s, loss=1.12] \n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.3284, Top1Accuracy: 4342/10000 (43%) Top5Accuracy: 7364.0/10000 (74%)\n\n\n\nFinished Training\n\nTraining complete in 6m 20s\n"}]},{"cell_type":"code","source":"import time\n\nresnet_basicblockLossv = []\nresnet_basicblockTop1AccuracyList = []   # top1准确率列表\nresnet_basicblockTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_basicblock = train(resnet_basicblock, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_basicblock, resnet_basicblockLossv,resnet_basicblockTop1AccuracyList,resnet_basicblockTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.execute_input":"2023-05-21T02:47:46.789213Z","iopub.status.busy":"2023-05-21T02:47:46.788808Z","iopub.status.idle":"2023-05-21T02:51:58.960508Z","shell.execute_reply":"2023-05-21T02:51:58.959190Z","shell.execute_reply.started":"2023-05-21T02:47:46.789178Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","output_type":"stream","text":"Epoch 0: 100%|██████████| 1250/1250 [00:43<00:00, 29.07it/s, loss=1.95] \n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.2133, Top1Accuracy: 4380/10000 (44%) Top5Accuracy: 7424.0/10000 (74%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 1: 100%|██████████| 1250/1250 [00:43<00:00, 28.84it/s, loss=1.24] \n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.4084, Top1Accuracy: 4323/10000 (43%) Top5Accuracy: 7323.0/10000 (73%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 2: 100%|██████████| 1250/1250 [00:42<00:00, 29.22it/s, loss=1.14] \n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.6218, Top1Accuracy: 4388/10000 (44%) Top5Accuracy: 7352.0/10000 (74%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 3: 100%|██████████| 1250/1250 [00:43<00:00, 28.68it/s, loss=0.478]\n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 3.0349, Top1Accuracy: 4320/10000 (43%) Top5Accuracy: 7195.0/10000 (72%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 4: 100%|██████████| 1250/1250 [00:43<00:00, 28.54it/s, loss=0.142] \n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 3.0746, Top1Accuracy: 4373/10000 (44%) Top5Accuracy: 7311.0/10000 (73%)\n\n\n\nFinished Training\n\nTraining complete in 3m 37s\n"}]},{"cell_type":"code","source":"import time\n\nresnet_seLossv = []\nresnet_seTop1AccuracyList = []   # top1准确率列表\nresnet_seTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_se = train(resnet_se, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_se, resnet_seLossv,resnet_seTop1AccuracyList,resnet_seTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.execute_input":"2023-05-21T08:25:33.671585Z","iopub.status.busy":"2023-05-21T08:25:33.671119Z","iopub.status.idle":"2023-05-21T08:33:10.269962Z","shell.execute_reply":"2023-05-21T08:33:10.268799Z","shell.execute_reply.started":"2023-05-21T08:25:33.671548Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stderr","output_type":"stream","text":"Epoch 0: 100%|██████████| 1250/1250 [01:23<00:00, 14.91it/s, loss=2.15]\n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.5215, Top1Accuracy: 3665/10000 (37%) Top5Accuracy: 6653.0/10000 (67%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 1: 100%|██████████| 1250/1250 [01:23<00:00, 14.98it/s, loss=2.55]\n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.4254, Top1Accuracy: 3793/10000 (38%) Top5Accuracy: 6900.0/10000 (69%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 2: 100%|██████████| 1250/1250 [01:23<00:00, 14.95it/s, loss=1.86]\n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.5326, Top1Accuracy: 3697/10000 (37%) Top5Accuracy: 6741.0/10000 (67%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 3: 100%|██████████| 1250/1250 [01:22<00:00, 15.07it/s, loss=1.62] \n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.3562, Top1Accuracy: 4100/10000 (41%) Top5Accuracy: 7082.0/10000 (71%)\n\n\n"},{"name":"stderr","output_type":"stream","text":"Epoch 4: 100%|██████████| 1250/1250 [01:23<00:00, 15.04it/s, loss=1.35] \n"},{"name":"stdout","output_type":"stream","text":"\n\nValidation set: Average loss: 2.3838, Top1Accuracy: 4203/10000 (42%) Top5Accuracy: 7194.0/10000 (72%)\n\n\n\nFinished Training\n\nTraining complete in 6m 57s\n"}]},{"cell_type":"code","source":"import time\n\ncola_netLossv = []\ncola_netTop1AccuracyList = []   # top1准确率列表\ncola_netTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    cola_net = train(cola_net, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(cola_net, cola_netLossv,cola_netTop1AccuracyList,cola_netTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-06-25T08:35:54.428334Z","iopub.execute_input":"2023-06-25T08:35:54.428773Z","iopub.status.idle":"2023-06-25T08:43:27.706765Z","shell.execute_reply.started":"2023-06-25T08:35:54.428726Z","shell.execute_reply":"2023-06-25T08:43:27.705446Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [01:19<00:00, 15.76it/s, loss=2]   \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2767, Top1Accuracy: 4074/10000 (41%) Top5Accuracy: 7134.0/10000 (71%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [01:21<00:00, 15.29it/s, loss=2.6] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2494, Top1Accuracy: 4244/10000 (42%) Top5Accuracy: 7319.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [01:21<00:00, 15.33it/s, loss=1.43] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1501, Top1Accuracy: 4480/10000 (45%) Top5Accuracy: 7520.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [01:21<00:00, 15.33it/s, loss=1.41] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2309, Top1Accuracy: 4530/10000 (45%) Top5Accuracy: 7459.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [01:22<00:00, 15.24it/s, loss=1.48] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2391, Top1Accuracy: 4546/10000 (45%) Top5Accuracy: 7551.0/10000 (76%)\n\nFinished Training\nTraining complete in 6m 46s\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}